Load

Libraries and functions

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”
==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data

MicrobiotaAgeSexsampleproject
GF Old Female GF_104w_F_1_2 S264
GF Old Female GF_104w_F_2_2 S264
GF Old Female GF_104w_F_3_2 S264
GF Old Male GF_104w_M_1_2 S264
GF Old Male GF_104w_M_2_2 S264
GF Old Male GF_104w_M_3_2 S288
GF Old Male GF_104w_M_4_2 S288
GF Old Male GF_104w_M_5_2 S288
GF Middle-aged Female GF_52w_F_1_2 S178
GF Middle-aged Female GF_52w_F_2_2 S178
GF Middle-aged Female GF_52w_F_3_2 S178
GF Middle-aged Female GF_52w_F_4_2 S178
GF Middle-aged Female GF_52w_F_5_2 S178
GF Middle-aged Female GF_52w_F_6_2 S178
GF Middle-aged Male GF_52w_M_1_2 S148
GF Middle-aged Male GF_52w_M_2_2 S148
GF Middle-aged Male GF_52w_M_3_2 S148
GF Middle-aged Male GF_52w_M_4_2 S148
GF Young Female GF_8w_F_1_2 S225
GF Young Female GF_8w_F_2_2 S225
GF Young Female GF_8w_F_3_2 S225
GF Young Female GF_8w_F_4_2 S225
GF Young Female GF_8w_F_5_2 S225
GF Young Male GF_8w_M_1_2 S148
GF Young Male GF_8w_M_2_2 S148
GF Young Male GF_8w_M_3_2 S148
GF Young Male GF_8w_M_4_2 S148
SPF Old Female SPF_104w_F_1_2S174
SPF Old Female SPF_104w_F_2_2S174
SPF Old Female SPF_104w_F_3_2S174
SPF Old Male SPF_104w_M_10_2S198
SPF Old Male SPF_104w_M_11_2S198
SPF Old Male SPF_104w_M_12_2S198
SPF Old Male SPF_104w_M_13_2S198
SPF Old Male SPF_104w_M_14_2S198
SPF Old Male SPF_104w_M_2_2 S174
SPF Old Male SPF_104w_M_3_2 S174
SPF Old Male SPF_104w_M_4_2 S174
SPF Old Male SPF_104w_M_5_2 S198
SPF Old Male SPF_104w_M_6_2 S198
SPF Old Male SPF_104w_M_7_2 S198
SPF Old Male SPF_104w_M_8_2 S198
SPF Old Male SPF_104w_M_9_2 S198
SPF Middle-aged Female SPF_52w_F_1_2 S178
SPF Middle-aged Female SPF_52w_F_2_2 S178
SPF Middle-aged Female SPF_52w_F_3_2 S178
SPF Middle-aged Female SPF_52w_F_4_2 S178
SPF Middle-aged Female SPF_52w_F_5_2 S178
SPF Middle-aged Female SPF_52w_F_6_2 S178
SPF Middle-aged Male SPF_52w_M_1_2 S148
SPF Middle-aged Male SPF_52w_M_2_2 S148
SPF Middle-aged Male SPF_52w_M_3_2 S148
SPF Middle-aged Male SPF_52w_M_4_2 S148
SPF Middle-aged Male SPF_52w_M_5_2 S148
SPF Young Female SPF_8w_F_1_2 S225
SPF Young Female SPF_8w_F_3_2 S225
SPF Young Female SPF_8w_F_4_2 S225
SPF Young Male SPF_8w_M_2_2 S148
SPF Young Male SPF_8w_M_3_2 S148
SPF Young Male SPF_8w_M_4_2 S148
Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”Warning message:
“Setting row names on a tibble is deprecated.”

Check samples and specific genes

Check for artefact due to FACS sorting

List of genes to check: Egr1, Jun, Zfp36l1, Malat1, Dusp1, Nr4a1, Fos

Normalized counts

Z-scores

Check for contamination from other cell types that could have escaped the sorting gating

cellgenes
microglia Tmem119
microglia Hexb
microglia P2ry12
microglia Siglech
microglia Trem2
microglia P2ry13
macrophage Mrc1
macrophage Cd163
macrophage Lyve1
macrophage Siglec1
macrophage Pf4
monocyte Ly6c2
monocyte Ccr2
monocyte Anxa8
monocyte Nr4a1
monocyte Plac8
dc Flt3
dc Zbtb46
dc Itgae
dc Batf3
dc Clec9a
granulocyteMpo
granulocyteNgp
granulocyteWfdc17
t Cd3e
t Cd3d
t Nkg7
b Cd79a
b Cd19
b Ebf1
mast Tpsb2
mast Cpa3
nk Gzmb
nk Eomes

Genes in list, but not in the counts

cellgenes
microglia Scl2a5
granulocyteLy6g
t Trbc1
t Trbc2
b Igkc
b Ighm
mast Mcpt4
mast Cma1
nk NKp46
nk NK1.1
nk NKG2D
nk Tbet

Normalized counts

cellgenes
microglia Tmem119
microglia Hexb
microglia P2ry12
microglia Siglech
microglia Trem2
microglia P2ry13
macrophage Mrc1
macrophage Cd163
macrophage Lyve1
macrophage Siglec1
macrophage Pf4
monocyte Ly6c2
monocyte Ccr2
monocyte Anxa8
monocyte Nr4a1
monocyte Plac8
dc Flt3
dc Zbtb46
dc Itgae
dc Batf3
dc Clec9a
granulocyteMpo
granulocyteNgp
granulocyteWfdc17
t Cd3e
t Cd3d
t Nkg7
b Cd79a
b Cd19
b Ebf1
mast Tpsb2
mast Cpa3
nk Gzmb
nk Eomes
cell
Tmem119microglia
Hexbmicroglia
P2ry12microglia
Siglechmicroglia
Trem2microglia
P2ry13microglia
Mrc1macrophage
Cd163macrophage
Lyve1macrophage
Siglec1macrophage
Pf4macrophage
Ly6c2monocyte
Ccr2monocyte
Anxa8monocyte
Nr4a1monocyte
Plac8monocyte
Flt3dc
Zbtb46dc
Itgaedc
Batf3dc
Clec9adc
Mpogranulocyte
Ngpgranulocyte
Wfdc17granulocyte
Cd3et
Cd3dt
Nkg7t
Cd79ab
Cd19b
Ebf1b
Tpsb2mast
Cpa3mast
Gzmbnk
Eomesnk
pdf: 2

Z-score

Filter contamination genes and contaminated samples

Genes that could be artefact due to FACS sorting or contamination from other cell types and samples that seem contaminated: 'GF_8w_M_2_2', 'SPF_52w_F_1_2', 'SPF_104w_M_3_2'

Filter the normalized counts (value before and )

61

Filter the samples from annotations and metadata

Sample clustering

Warning message:
“Setting row names on a tibble is deprecated.”

With all genes

Clustering method: Ward D2

pdf: 2

Without X/Y genes

Extract genes from chrX / chrX_GL456233_random / chrY

Warning message:
“Too many values at 30003 locations: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...”
1120

Remove genes (number displayed) from the count table

696

Clustering method: Ward D2

PCA on the normalized counts

With all factors

Warning message:
“Setting row names on a tibble is deprecated.”
MicrobiotaAgeSex
GF Old Female
GF Old Female
GF Old Female
GF Old Male
GF Old Male
GF Old Male
$Microbiota
SPF
'#65ce41'
GF
'#c3a7f9'
$Age
Young
'#ff82ec'
Middle-aged
'#00dcb2'
Old
'#e2b33a'
$Sex
Female
'#ff918b'
Male
'#00d2fb'

SPF vs GF

Male & Young

Saving 7 x 7 in image

Male & Middle-aged

Saving 7 x 7 in image

Male & Old

Saving 7 x 7 in image

Female & Young

Saving 7 x 7 in image

Female & Middle-aged

Saving 7 x 7 in image

Female & Old

Saving 7 x 7 in image

Z-scores (normalized counts) for all genes

All genes

Column order: microbiota - sex - age

pdf: 2

Subset of selected genes

GF signature genes

Column order: microbiota - sex - age

pdf: 2

DNA damage repair genes

Column order: microbiota - sex - age

pdf: 2
pdf: 2

Gene co-expression analysis

Weighted gene co-expression network analysis using WGCNA package

Extract modules of co-expressed genes

Keep only genes that have a count >= 10 in more than 90% of the samples (number removed / kept displayed)

9417
10277
  1. 10277
  2. 58

Analysis of scale free topology for multiple soft thresholding powers, with signed hybrid network type

pickSoftThreshold: will use block size 4353.
 pickSoftThreshold: calculating connectivity for given powers...
   ..working on genes 1 through 4353 of 10277
   ..working on genes 4354 through 8706 of 10277
   ..working on genes 8707 through 10277 of 10277
   Power SFT.R.sq  slope truncated.R.sq  mean.k. median.k. max.k.
1      1    0.116  0.852          0.943 1040.000  1.04e+03 1630.0
2      2    0.353 -1.100          0.941  336.000  3.18e+02  821.0
3      3    0.664 -1.650          0.978  136.000  1.17e+02  496.0
4      4    0.769 -1.920          0.983   64.300  4.86e+01  332.0
5      5    0.806 -2.060          0.979   33.700  2.18e+01  238.0
6      6    0.813 -2.140          0.969   19.100  1.04e+01  178.0
7      7    0.817 -2.190          0.965   11.600  5.21e+00  137.0
8      8    0.842 -2.150          0.975    7.330  2.71e+00  108.0
9      9    0.864 -2.100          0.983    4.840  1.47e+00   87.5
10    10    0.876 -2.060          0.986    3.310  8.04e-01   71.6
11    12    0.897 -1.990          0.991    1.670  2.64e-01   49.7
12    14    0.904 -1.930          0.991    0.921  9.27e-02   35.7
13    16    0.919 -1.870          0.994    0.541  3.43e-02   26.4
14    18    0.930 -1.810          0.997    0.335  1.34e-02   19.9
15    20    0.940 -1.760          0.998    0.216  5.39e-03   15.2
PowerSFT.R.sqslopetruncated.R.sqmean.k.median.k.max.k.
1 0.1160946 0.8516606 0.9432081 1043.23197431.039858e+031631.43013
2 0.3534325 -1.0957144 0.9406479 335.81363833.181957e+02 820.65787
3 0.6637248 -1.6460594 0.9780977 136.38819671.174060e+02 496.21168
4 0.7685466 -1.9220353 0.9832291 64.31907084.862867e+01 332.43621
5 0.8057060 -2.0564238 0.9792358 33.71886882.180012e+01 237.66372
6 0.8134695 -2.1404068 0.9685734 19.13648601.041504e+01 177.64476
7 0.8167563 -2.1947647 0.9653164 11.55236995.214321e+00 137.13604
8 0.8422749 -2.1489288 0.9752509 7.32730562.709162e+00 108.47588
9 0.8638155 -2.1041594 0.9828091 4.83919561.465842e+00 87.45249
10 0.8760665 -2.0646700 0.9856232 3.30534528.042653e-01 71.58565
12 0.8974660 -1.9875884 0.9909974 1.67249372.641405e-01 49.69122
14 0.9036729 -1.9326232 0.9905956 0.92116289.265626e-02 35.74188
16 0.9189432 -1.8686196 0.9937579 0.54149043.432230e-02 26.39679
18 0.9297761 -1.8072410 0.9965048 0.33515971.342172e-02 19.89803
20 0.9401800 -1.7582833 0.9982447 0.21632965.386947e-03 15.24642
Powermean.k.
5 5 33.7188688
6 6 19.1364860
7 7 11.5523699
8 8 7.3273056
9 9 4.8391956
1010 3.3053452
1112 1.6724937
1214 0.9211628
1316 0.5414904
1418 0.3351597
1520 0.2163296

Block-wise network construction and module detection

 Calculating module eigengenes block-wise from all genes
   Flagging genes and samples with too many missing values...
    ..step 1
 ..Working on block 1 .
    TOM calculation: adjacency..
    ..will use 4 parallel threads.
     Fraction of slow calculations: 0.000000
    ..connectivity..
    ..matrix multiplication (system BLAS)..
    ..normalization..
    ..done.
   ..saving TOM for block 1 into file norm_genes_TOM-block.1.RData
 ....clustering..
 ....detecting modules..
 ....calculating module eigengenes..
 ....checking kME in modules..
     ..removing 45 genes from module 1 because their KME is too low.
     ..removing 27 genes from module 2 because their KME is too low.
     ..removing 19 genes from module 3 because their KME is too low.
     ..removing 7 genes from module 4 because their KME is too low.
     ..removing 1 genes from module 5 because their KME is too low.
     ..removing 2 genes from module 6 because their KME is too low.
     ..removing 4 genes from module 7 because their KME is too low.
     ..removing 4 genes from module 8 because their KME is too low.
     ..removing 1 genes from module 9 because their KME is too low.
     ..removing 1 genes from module 10 because their KME is too low.
     ..removing 1 genes from module 11 because their KME is too low.
     ..removing 1 genes from module 14 because their KME is too low.
     ..removing 1 genes from module 15 because their KME is too low.
     ..removing 1 genes from module 16 because their KME is too low.
     ..removing 2 genes from module 18 because their KME is too low.
     ..removing 1 genes from module 24 because their KME is too low.
 ..merging modules that are too close..
     mergeCloseModules: Merging modules whose distance is less than 0.35
       Calculating new MEs...

Size of the modules (ME0: genes not assigned to a module) and number of genes in modules

 ME0  ME1  ME2  ME3  ME4  ME5  ME6  ME7  ME8  ME9 
2719 1627 1240 1221 1123  925  467  467  371  117 

Dendrogram and the module colors underneath the block

pdf: 2

Associate module color to genes

0610007P14Rik
5
0610009B22Rik
5
0610009L18Rik
0
0610009O20Rik
6
0610010F05Rik
0
0610010K14Rik
5

Recalculate MEs with color labels

Extract the palette for next plots

ME0
'grey'
ME1
'turquoise'
ME2
'blue'
ME3
'brown'
ME4
'yellow'
ME5
'green'
ME6
'red'
ME7
'black'
ME8
'pink'
ME9
'magenta'

Relationship between modules and samples

Module-trait correlation analysis between the module eigengene (ME) and the different trait (combination of Microbiota, age and sex)

  • correlation of Pearson between each pair of variables
  • Student asymptotic p-values for the correlations
ME0ME1ME2ME3ME4ME5ME6ME7ME8ME9
0.05628294 -0.15484098 0.24580761 -0.05146022 -0.03773412 0.1487267 0.0017992870.10250437 -0.08009466 0.17194013
-0.01131610 -0.15544628 0.18694173 -0.04086877 -0.02657179 0.1285656 0.0014605540.05570285 -0.08204890 0.03501303
-0.01916005 -0.16952548 0.22087210 -0.03977326 -0.04249419 0.2615200 -0.0486042200.10491770 -0.02830703 0.17742336
-0.10518342 -0.14685257 0.09100536 0.04528510 -0.07699816 0.2489430 -0.1094315660.07330849 -0.01047623 0.11103972
-0.08524261 -0.20852637 0.20740756 -0.03923631 -0.03795286 0.3546826 -0.0905568390.06341630 -0.06267479 0.19417460
0.26432465 -0.05375806 0.11632265 -0.03499886 -0.05315761 -0.1751008 0.2109005800.13661031 -0.04028146 -0.18311995
sampleYoung / SPF / MaleYoung / SPF / FemaleYoung / GF / MaleYoung / GF / FemaleMiddle-aged / SPF / MaleMiddle-aged / SPF / FemaleMiddle-aged / GF / MaleMiddle-aged / GF / FemaleOld / SPF / MaleOld / SPF / FemaleOld / GF / MaleOld / GF / Female
GF_104w_F_1_20 0 0 0 0 0 0 0 0 0 0 1
GF_104w_F_2_20 0 0 0 0 0 0 0 0 0 0 1
GF_104w_F_3_20 0 0 0 0 0 0 0 0 0 0 1
GF_104w_M_1_20 0 0 0 0 0 0 0 0 0 1 0
GF_104w_M_2_20 0 0 0 0 0 0 0 0 0 1 0
GF_104w_M_3_20 0 0 0 0 0 0 0 0 0 1 0
Young / SPF / MaleYoung / SPF / FemaleYoung / GF / MaleYoung / GF / FemaleMiddle-aged / SPF / MaleMiddle-aged / SPF / FemaleMiddle-aged / GF / MaleMiddle-aged / GF / FemaleOld / SPF / MaleOld / SPF / FemaleOld / GF / MaleOld / GF / Female
ME0-0.2267303 -0.33425765 -0.02070888 -0.4334783 -0.06239905 0.24833823 0.10313044 0.08745041 -0.009537167 0.25005209 0.32333113 0.01530051
ME1 0.2444567 -0.05147813 0.21252423 -0.2385979 0.33940561 -0.21373138 0.29513458 -0.37536960 0.392059078-0.10840018 -0.33871084 -0.28447480
ME2-0.1107945 -0.07434626 0.01312926 0.1756570 -0.24897784 0.02357208 -0.03498967 0.35254655 -0.587033526-0.02985687 0.42451098 0.38752374
ME3-0.2432377 0.17729366 -0.33148537 0.2024006 -0.31676621 -0.01390937 -0.34189544 0.07365013 0.564464845-0.04514973 -0.07060893 -0.07832172
ME4 0.3959442 -0.14002887 0.40735863 -0.1778602 0.46755929 -0.15742481 0.41011754 -0.16627167 -0.407676082-0.09449454 -0.11525209 -0.06332041
ME5-0.1430446 0.17595879 -0.09674862 0.3796193 -0.34483042 -0.10883820 -0.30665340 0.19281946 -0.035916924-0.10936952 0.07964452 0.31945486
pdf: 2
Young / SPF / Male
3
Young / SPF / Female
3
Young / GF / Male
3
Young / GF / Female
5
Middle-aged / SPF / Male
5
Middle-aged / SPF / Female
5
Middle-aged / GF / Male
4
Middle-aged / GF / Female
6
Old / SPF / Male
13
Old / SPF / Female
3
Old / GF / Male
5
Old / GF / Female
3

Genes in modules

Associate genes to modules

Heatmaps with Z-scores

Microbiota / Sex / Age

pdf: 2

Sex / Microbiota / Age

Age / Microbiota / Sex

Sinaplots of the Z-scores per groups

The mean of the Z-score over the samples in the group is plot for each gene

Microbiota / Age

Microbiota / Age / Sex

Age / Microbiota / Sex

Enrichment analysis in modules

Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”
[1] "ME0"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
[1] category                 over_represented_pvalue  under_represented_pvalue
[4] numDEInCat               numInCat                 term                    
[7] ontology                
<0 rows> (or 0-length row.names)
[1] "ME1"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
12376 GO:0051252            4.278302e-06                 0.999997        436
5808  GO:0019219            1.340659e-05                 0.999990        469
      numInCat                                                           term
12376     2015                            regulation of RNA metabolic process
5808      2222 regulation of nucleobase-containing compound metabolic process
      ontology
12376       BP
5808        BP
[1] "ME2"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
10532 GO:0044712            2.207706e-09                1.0000000         97
10522 GO:0044699            1.999958e-08                1.0000000        825
10548 GO:0044763            9.184695e-08                1.0000000        741
10530 GO:0044710            6.896704e-07                0.9999997        326
4111  GO:0009987            8.402822e-07                1.0000000        997
3796  GO:0009056            1.097148e-06                0.9999996        185
      numInCat                              term ontology
10532      419 single-organism catabolic process       BP
10522     6250           single-organism process       BP
10548     5618  single-organism cellular process       BP
10530     2074 single-organism metabolic process       BP
4111      7767                  cellular process       BP
3796      1105                 catabolic process       BP
[1] "ME3"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
2888  GO:0006643            3.133964e-07                0.9999999         34
11236 GO:0046467            4.161297e-07                0.9999999         26
9239  GO:0036503            2.630508e-06                0.9999993         25
6569  GO:0030433            2.869772e-06                0.9999993         22
16085 GO:1901137            3.216894e-06                0.9999986         74
10364 GO:0044255            6.599740e-06                0.9999966         94
      numInCat                                                        term
2888       113                            membrane lipid metabolic process
11236       76                         membrane lipid biosynthetic process
9239        78                                                ERAD pathway
6569        64 ER-associated ubiquitin-dependent protein catabolic process
16085      366                carbohydrate derivative biosynthetic process
10364      498                            cellular lipid metabolic process
      ontology
2888        BP
11236       BP
9239        BP
6569        BP
16085       BP
10364       BP
[1] "ME4"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
15289 GO:0090304            2.368936e-09                        1        410
12323 GO:0051171            9.607771e-09                        1        342
5808  GO:0019219            1.129381e-08                        1        322
12376 GO:0051252            1.770608e-08                        1        296
5008  GO:0016070            5.693565e-08                        1        363
2572  GO:0006139            5.874381e-08                        1        436
      numInCat                                                           term
15289     2972                                 nucleic acid metabolic process
12323     2391              regulation of nitrogen compound metabolic process
5808      2222 regulation of nucleobase-containing compound metabolic process
12376     2015                            regulation of RNA metabolic process
5008      2637                                          RNA metabolic process
2572      3297               nucleobase-containing compound metabolic process
      ontology
15289       BP
12323       BP
5808        BP
12376       BP
5008        BP
2572        BP
[1] "ME5"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
2994  GO:0006810            2.939130e-07                        1        258
17355 GO:1904872            3.385056e-07                        1         10
17356 GO:1904874            3.385056e-07                        1         10
12329 GO:0051179            4.691320e-07                        1        302
3111  GO:0007005            5.407070e-07                        1         98
15398 GO:0090670            6.918322e-07                        1         10
      numInCat                                                             term
2994      2389                                                        transport
17355       15          regulation of telomerase RNA localization to Cajal body
17356       15 positive regulation of telomerase RNA localization to Cajal body
12329     3039                                                     localization
3111       484                                       mitochondrion organization
15398       16                                   RNA localization to Cajal body
      ontology
2994        BP
17355       BP
17356       BP
12329       BP
3111        BP
15398       BP
[1] "ME6"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
12089 GO:0050789            8.106605e-09                1.0000000        318
12093 GO:0050794            2.640981e-08                1.0000000        306
13804 GO:0065007            2.746683e-08                1.0000000        327
5008  GO:0016070            1.204085e-05                0.9999926        171
2678  GO:0006351            1.946611e-05                0.9999881        140
15658 GO:0097659            2.090775e-05                0.9999872        140
      numInCat                                 term ontology
12089     5258     regulation of biological process       BP
12093     5036       regulation of cellular process       BP
13804     5503                biological regulation       BP
5008      2637                RNA metabolic process       BP
2678      1986         transcription, DNA-templated       BP
15658     1989 nucleic acid-templated transcription       BP
[1] "ME7"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Warning message in pcls(G):
“initial point very close to some inequality constraints”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
10530 GO:0044710            1.731221e-08                1.0000000        144
2477  GO:0005975            6.281050e-07                0.9999998         37
16083 GO:1901135            1.324332e-06                0.9999995         53
2994  GO:0006810            1.464871e-06                0.9999994        154
1111  GO:0002715            3.013966e-06                0.9999998          8
9466  GO:0042269            3.013966e-06                0.9999998          8
      numInCat                                                    term ontology
10530     2074                       single-organism metabolic process       BP
2477       325                          carbohydrate metabolic process       BP
16083      591               carbohydrate derivative metabolic process       BP
2994      2389                                               transport       BP
1111        21     regulation of natural killer cell mediated immunity       BP
9466        21 regulation of natural killer cell mediated cytotoxicity       BP
[1] "ME8"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
3084  GO:0006955            9.051452e-24                        1         76
931   GO:0002376            1.056766e-20                        1        106
3081  GO:0006952            9.658779e-19                        1         72
4022  GO:0009605            6.422007e-16                        1         81
10659 GO:0045087            5.305911e-15                        1         44
8170  GO:0034097            2.014058e-14                        1         42
      numInCat                          term ontology
3084       586               immune response       BP
931       1171         immune system process       BP
3081       644              defense response       BP
4022       886 response to external stimulus       BP
10659      317        innate immune response       BP
8170       302          response to cytokine       BP
[1] "ME9"
Warning message in grep(txdbPattern, installedPackages):
“argument 'pattern' has length > 1 and only the first element will be used”Fetching GO annotations...
For 171 genes, we could not find any categories. These genes will be excluded.
To force their use, please run with use_genes_without_cat=TRUE (see documentation).
This was the default behavior for version 1.15.1 and earlier.
Calculating the p-values...
'select()' returned 1:1 mapping between keys and columns
        category over_represented_pvalue under_represented_pvalue numDEInCat
2721  GO:0006412            4.404280e-21                        1         44
9837  GO:0043043            6.708096e-21                        1         44
10141 GO:0043604            1.507733e-20                        1         44
10140 GO:0043603            2.622072e-19                        1         45
2794  GO:0006518            5.760471e-19                        1         44
16192 GO:1901566            2.044808e-15                        1         45
      numInCat                                         term ontology
2721       462                                  translation       BP
9837       472                 peptide biosynthetic process       BP
10141      510                   amide biosynthetic process       BP
10140      615             cellular amide metabolic process       BP
2794       548                    peptide metabolic process       BP
16192      776 organonitrogen compound biosynthetic process       BP

Citations

To cite WGCNA in publications use:

  Langfelder P and Horvath S, WGCNA: an R package for weighted
  correlation network analysis. BMC Bioinformatics 2008, 9:559
  doi:10.1186/1471-2105-9-559

  Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust
  Correlations and Hierarchical Clustering. Journal of Statistical
  Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/.

We have invested a lot of time and effort in creating the package,
please cite it when using it for data analysis.
To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.
To cite sinaplot in publications, please use:

  Sidiropoulos. N, Sohi S.H., Rapin N., Bagger F.O. (2017). An Enhanced
  Chart for Simple and Truthful Representation of Single Observations
  over Multiple Classes, R package version 1.1.0.

  Sidiropoulos. N, Sohi S.H., Rapin N., Bagger F.O., SinaPlot: an
  enhanced chart for simple and truthful representation of single
  observations over multiple classes. bioRxiv doi:
  http://dx.doi.org/10.1101/028191

To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.
@Manual{,
  title = {An Enhanced Chart for Simple and Truthful Representation of
Single Observations over Multiple Classes},
  author = {Sidiropoulos N. and Sohi S.H. and Rapin N. and Bagger F.O.},
  publisher = {manual},
  year = {2017},
  note = {R package version 1.1.0},
  url = {https://cran.r-project.org/package=sinaplot},
}

@TechReport{,
  title = {SinaPlot: an enhanced chart for simple and truthful representation of single observations over multiple classes},
  author = {Nikos Sidiropoulos and Sina Hadi Sohi and Nicolas Rapin and Frederik Otzen Bagger},
  booktitle = {bioRxiv},
  institution = {Cold Spring Harbor Labs Journals},
  year = {2015},
  month = {Oct},
  doi = {10.1101/028191},
  url = {http://biorxiv.org/content/early/2015/10/02/028191.abstract},
}
The methods within the code package can be cited as:

  Young, M.D., Wakefield, M.J., Smyth, G.K., Oshlack, A., Gene ontology
  analysis for RNA-seq: accounting for selection bias, Genome Biology,
  11, 2, Feb 2010, R14

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Gene ontology analysis for RNA-seq: accounting for selection bias},
    author = {Matthew D Young and Matthew J Wakefield and Gordon K Smyth and Alicia Oshlack},
    journal = {Genome Biology},
    volume = {11},
    pages = {R14},
    year = {2010},
  }

This free open-source software implements academic research by the
authors and co-workers. If you use it, please support the project by
citing the appropriate journal articles.
Warning message in citation("org.Mm.eg.db"):
“no date field in DESCRIPTION file of package ‘org.Mm.eg.db’”
To cite package ‘org.Mm.eg.db’ in publications use:

  Marc Carlson (2016). org.Mm.eg.db: Genome wide annotation for Mouse.
  R package version 3.4.0.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {org.Mm.eg.db: Genome wide annotation for Mouse},
    author = {Marc Carlson},
    year = {2016},
    note = {R package version 3.4.0},
  }

ATTENTION: This citation information has been auto-generated from the
package DESCRIPTION file and may need manual editing, see
‘help("citation")’.